Statistical characterization of time variability in midlatitude single‐tone HF channel response

Arıkan, Feza; Erol, Cemil B.

doi:10.1029/98rs01889

Cited by 9 publications

(12 citation statements)

References 5 publications

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“…Reference [7] proposes a statistical analysis method to obtain the statistical characterization of the time variability of a HF channel response. The method employs sliding window statistical analysis to estimate the time-varying first order (mean) and second order (variance) moments of the channel response.…”

Section: Estimation Of Wss Periodmentioning

confidence: 99%

“…If the TUP of a time-varying process is chosen as the local WSS period, there will be no significant variation during the TUP, and the significant changes will be observed in time intervals that having the same scale with the TUP. In [7], a statistical analysis approach is proposed to obtain the statistical characterization of the time variability of a HF channel response. The method employs sliding window statistical analysis to estimate the time-varying mean and variance in order to capture the temporal variability.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of optimum temporal update periods for regional TEC monitoring

Sayin

Akdoğan

Arıkan

2009

2009 4th International Conference on Recent Advances in Space Technologies

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Monitoring of the ionospheric variability is necessary for improving the performance of communication, navigation and positioning systems. Total Electron Content (TEC) is an important parameter since it can be used to analyze the variability of the ionosphere. Due to sparse distribution of samples, TEC can not be directly obtained everywhere on the ionosphere and accurate interpolation methods are needed for generating TEC maps. The optimum temporal update period (TUP) for regional Total Electron Content (TEC) monitoring has to be determined for capturing significant variations. In this study, the wide sense stationary (WSS) period is investigated whether it is possible to use the estimated WSS periods as the TUPs of the regional TEC maps. The sliding window statistical analysis method is used to estimate the WSS period. The WSS periods are compared with the difference between the maps obtained by Ordinary Kriging (OK) method. It is observed that the maximum WSS periods are following the length of time delays between the TEC maps at which the first significant difference occurs. The minimum WSS periods have to be used if the small variations are needed to be monitored within the day of interest. The WSS periods can be used as the TUPs for the regional TEC monitoring and can be adaptively adjusted.

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Section: Estimation Of Wss Periodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of optimum temporal update periods for regional TEC monitoring

Sayin

Akdoğan

Arıkan

2009

2009 4th International Conference on Recent Advances in Space Technologies

Self Cite

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“…In the above state‐space model, h n can vary within a pulse interval. However, in HF applications the time variation of the channel response can be safely modeled to vary on a pulse‐to‐pulse basis [ Arikan and Erol , 1998]. Under this assumption, we can obtain the following simplified model where variations in h is modelled to take place from pulse‐to‐pulse, where the impulse response of the ionosphere is allowed to vary according to u p , a predefined time variation on h p as where the subscript p denotes the p th pulse.…”

Section: Estimation Of Baseband Channel Responsementioning

confidence: 99%

“…With this initialization procedure, the Kalman Filter can operate with an initial estimate which minimizes the error between the actual channel and the estimated impulse response, provided that the channel is slowly varying at least for couple of pulses used in the initialization. This assumption seems to be valid for midlatitude links whose wide sense stationarity period is found to be in the order of 20 s [ Arikan and Erol , 1998]. This delay in estimation causes the first channel estimate to be available only after first LM samples length of channel impulse response.…”

Section: Estimation Of Baseband Channel Responsementioning

confidence: 99%

Adaptive tracking of narrowband HF channel response

Arıkan

2003

Radio Science

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[1] Estimation of channel impulse response constitutes a first step in computation of scattering function, channel equalization, elimination of multipath, and optimum detection and identification of transmitted signals through the HF channel. Due to spatial and temporal variations, HF channel impulse response has to be estimated adaptively. Based on developed state-space and measurement models, an adaptive Kalman filter is proposed to track the HF channel variation in time. Robust methods of initialization and adaptively adjusting the noise covariance in the system dynamics are proposed. In simulated examples under good, moderate and poor ionospheric conditions, it is observed that the adaptive Kalman filter based channel estimator provides reliable channel estimates and can track the variation of the channel in time with high accuracy.

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“…If the WSS period is chosen as the TUP, then significant variations of the regional ionosphere can be captured. In the work of Arikan and Erol [1998], an efficient tool is developed to obtain the statistical characterization of the time variability of HF channel response. The method employs sliding window statistical analysis to estimate the time‐varying mean and variance to capture the temporal variability.…”

Section: Introductionmentioning

confidence: 99%

Optimum temporal update periods for regional ionosphere monitoring

2010

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[1] Monitoring of the ionospheric variability is necessary for improving the performance of communication, navigation, and positioning systems. Total electron content (TEC) is an important parameter in analyzing the variability of the ionosphere. Owing to sparse distribution of TEC in a given region, spatial interpolation methods are used for mapping TEC on a dense grid. Typically, TEC maps are produced at time intervals that do not match the variability of the regional ionosphere. To capture the local, small-scale variability, the temporal update period (TUP) in regional TEC monitoring has to be optimized. In this study, the wide sense stationarity (WSS) period is proposed to be used as the optimum TUP of the regional TEC maps. Individual WSS periods of TEC are obtained by the sliding window statistical analysis method. Four types of measures are employed to compute the differences between TEC maps. When WSS periods of the sampling points and the differences of TEC maps are compared with each other, it is observed that for the quiet days of the ionosphere where the general temporal trends dominate, the maximum WSS period in the region can be chosen as the optimum TUP. For the disturbed days of the ionosphere where high temporal and spatial variability is observed, averages or the minimums of WSS periods in a given region have to be chosen as the optimum TUP. Thus, WSS period can be developed into a useful tool in monitoring the ionospheric variability in a given region.

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Statistical characterization of time variability in midlatitude single‐tone HF channel response

Cited by 9 publications

References 5 publications

Investigation of optimum temporal update periods for regional TEC monitoring

Investigation of optimum temporal update periods for regional TEC monitoring

Adaptive tracking of narrowband HF channel response

Optimum temporal update periods for regional ionosphere monitoring

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